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Highly recommend you install the GPU version of PaddlePaddle, due to the large overhead of segmentation models, otherwise, it could be out of memory while running the models.
# CUDA10.1
python -m pip install paddlepaddle-gpu==2.2.1.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
# CPU
python -m pip install paddlepaddle==2.2.1 -i https://mirror.baidu.com/pypi/simple
Please make sure that your PaddlePaddle is installed successfully and the version is not lower than the required version. Use the following command to verify.
# Confirm that PaddlePaddle is installed successfully in your Python interpreter
>>> import paddle
>>> paddle.utils.run_check()
# Confirm PaddlePaddle version
python -c "import paddle; print(paddle.__version__)"
# If the following prompt appears on the command line, the PaddlePaddle installation is successful.
# PaddlePaddle is installed successfully! Let's start deep learning with PaddlePaddle now.
Support API method for flexible development.
pip install paddleseg
Support Configuration Drive for simple and fast development.
git clone https://github.com/PaddlePaddle/PaddleSeg
cd PaddleSeg
pip install -r requirements.txt
#If a version error occurs during installation, you can try to delete the old version and re-run the script.
Run the following command to verify PaddleSeg installation. The predicted results will be in output/result if successful.
python predict.py \
--config configs/quick_start/bisenet_optic_disc_512x512_1k.yml \
--model_path https://bj.bcebos.com/paddleseg/dygraph/optic_disc/bisenet_optic_disc_512x512_1k/model.pdparams\
--image_path docs/images/optic_test_image.jpg \
--save_dir output/result
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